The topic of data quality is a perennial issue in German companies. Data is the raw material for analyses, forecasts and strategic decisions. Efficient reporting cannot be achieved without clean
However, the data maintenance required for this does not meet with much approval. Hardly any company devotes the appropriate attention to this topic. In most cases, only a minimum of data maintenance takes place. Only the most important data is ever entered into the ERP system. The rest only receives sporadic attention or is completely ignored. The fear that their own employees will reject restrictive specifications is too great.
This is the paradox that many companies are confronted with: Everyone wants the highest possible data quality – but hardly anyone wants to do anything about it.
Lack of foresight and inadequate data maintenance go hand in hand
How can this paradox be resolved? How should decision-makers proceed if they want to anchor data maintenance in their organization’s processes? To answer this question, we first need to find out why data management is such a low priority in companies. There are essentially two perspectives here: one organizational and one human.
Companies should start integrating data maintenance into their processes now. This is the only way to ensure that they can respond to a crisis with accurate analyses at any time.
From an organizational point of view, poor data quality is always the result of poor processes. Data maintenance is simply not part of the business processes. There are no guidelines regarding complete data entry or regular review of the data stock and no one to check such guidelines. In many companies, data maintenance is optional. And that is fatal for the accuracy of data entry.
In most cases, the reason for such a situation is quite simple: until now, data maintenance has never been important. High data quality ensures efficiency – but it is not a must. Many companies have no need for optimization. Their business is going well, turnover is good and orders are coming in like an assembly line. What still needs to be improved?
The problems only start when the company’s situation deteriorates. As soon as sales plummet, good advice is expensive. Process optimization suddenly becomes relevant. But without adequate data quality, this goal is difficult to achieve.
Data maintenance is considered a superfluous work step
Then there is the human perspective. Even if data maintenance is firmly anchored in the company’s processes, this does not necessarily mean that it is practiced. It is often the employees who, despite guidelines, take little care when entering data. And who can blame them? From the employees’ point of view, data maintenance is just an additional work step that makes their daily routine more complex. Often they don’t even benefit from it.
The effect of regular data maintenance only becomes apparent when we look at the company as a whole. In most cases, one department benefits from the diligence of the rest of the organization. And that’s what makes it so difficult to get colleagues to do it.
Why should I spend my time on data maintenance if I get nothing out of it? I may be suffering from the lack of diligence of another department despite my extra effort. If I only deal with data maintenance sporadically, I make my life easier. A classic case from game theory.
Data drives modern companies
So now we have identified the central problems. What can companies do to counteract them?
Step one is to make the effects of poor data quality visible. Data is an important resource in the modern business world. Incorrect or incomplete data sets affect the entire organization. Here are a few examples:
- Detailed planning must know exactly which work steps a production item should go through in which order and how much time is required for this. If your data is not precise enough, there will be constant delays. A lack of data can even make software-supported planning impossible.
- In merchandise management , your team monitors which items and components are available in which quantities and which incoming and outgoing goods are expected in the near future. If your database is not sufficiently maintained, there will be delays in production,
unnecessarily high stock levels or delivery problems. The result: dissatisfied customers and high storage costs. - Marketing and sales use the CRM system to target potential customers precisely. Incorrect or inaccurate data means that contacts receive unsuitable marketing information or are not contacted by sales in good time. Important sales potential therefore remains untapped.
- Controlling and corporate management rely on individually configurable KPI cockpits that provide users with an overview of selected KPIs at all times. If the underlying database contains errors, the calculated key figures are not precise enough to serve as a decision-making aid.
Data maintenance can only be anchored if everyone participates
Basically, there is no way around a restrictive approach. As long as data maintenance is not firmly integrated into your internal processes , it will not be practiced. Your goal must therefore be to introduce and monitor guidelines for data entry. Without clear guidelines, data maintenance will always remain an optional extra step. And if something is optional, there will be employees who do not adhere to it.
If you want to anchor data maintenance in the company, you need to get everyone involved on board, both the management and the employees. If, on the other hand, you decide over their heads, you can already prepare yourself for resistance.
As far as the management level is concerned, in our experience, the best way is through a risk analysis. Put the further development of the company on the agenda and address potential risks.
What happens, for example, when international competitors enter the market? Or how likely are technological developments that threaten your business model? As soon as you have identified a threat scenario, the need for reliable figures becomes self-evident. After all, without detailed knowledge of your own organization and its environment, you cannot develop any countermeasures.
Data maintenance in the ERP system ensures the company’s success.
In turn, you can get your employees on board by creating cross-departmental awareness of the importance of data quality . To do this, bring all departments together and discuss in detail the impact that a lack of discipline in data entry has on other areas.
Point out that errors in data entry make life difficult for colleagues elsewhere. This allows you to transform abstract specifications into real-life problems. In addition, you create a motivating sense of community that makes it difficult for individuals to isolate themselves. A network of cooperation is created: All employees support each other and benefit from the data maintenance of others.
Automation helps with data maintenance
If you want to establish data maintenance in your company, a good approach is to make it as uncomplicated as possible. The less additional work involved, the easier it will be to convince your team. Your ERP solution can be a great help here. This is because most modern ERP systems are capable of (partially) automating routine tasks. Employees are often more likely to accept specifications regarding data maintenance if this means no (or only minimal) additional work.
Plausibility checks during input are an example of automated data maintenance. Or you can configure the system so that it deletes or merges duplicate entries at regular intervals. Of course, you can also go one step further and ensure that certain work steps are recorded fully automatically in the ERP system. One example of this is the automatic reordering of materials at the touch of a button as soon as stocks run low. In this case, your employees no longer need to enter any data manually in the ERP system.
But be careful: an ERP system needs a minimum level of data quality for error-free operation. Without adequate input, you won’t get any useful figures from the system – “trash in, trash out” is what IT experts call it. Before introducing ERP, you should therefore go through your database meticulously, add missing data, delete duplicate entries and update outdated data records. This is time-consuming. But if you want to improve your data quality in the long term, there is no way around it.
Start now
Data maintenance is not an attractive topic for most companies. It often triggers boredom rather than understanding. Nevertheless, you should take care of it sooner rather than later. Because it is in the nature of things that data maintenance only becomes relevant when the child has already fallen into the well. By then, however, it is already too late. As soon as sales fall rapidly, you need a precise analysis of the causes. However, you can only carry out such an analysis if you have already paid attention to the quality of your master data beforehand.
Optimal data quality is by no means a marginal issue that you can devote yourself to at some point in the future. You should start integrating data maintenance into your processes now. Make sure that careful data entry becomes an integral part of all business processes. This is the only way you can counter a crisis with accurate analyses at any time.
As soon as your databases are clean and well maintained, you have also fulfilled an important prerequisite for the ERP implementation. If you would like to know how an ERP project works in detail, we recommend our Whitepaper “The ERP implementation from A to Z.”. It explains everything you need to know.
